Systems and Methods for Composite Dose Quality Assurance with Three-Dimensional Arrays

ABSTRACT

A method for performing composite dose quality assurance with a three-dimensional (3D) radiation detector array includes delivering a radiation fraction to the 3D array according to a radiation treatment (RT) plan, measuring absolute dose per detector of the 3D array, per unit of time, determining a radiation source emission angle per unit of time, synchronizing the RT plan with the measured absolute doses and determined radiation source emission angles to determine an absolute time for a control point of each beam of the synchronized RT plan, converting the beams of the synchronized RT plan into a series of sub-beams, generating a 3D relative dose grid for each of the sub-beams, applying a calibration factor grid to each of the 3D relative dose grids to determine a 3D absolute dose grid for each of the sub-beams, summing the 3D absolute dose grids to generate a 3D absolute dose deposited in the 3D array, and determining a 3D dose correction grid for application to the RT plan based on the 3D absolute dose.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims the benefit of U.S. Provisional Application Ser.No. 61/708,916, filed on Oct. 2, 2012, the contents of which are hereinincorporated by reference in their entirety.

FIELD OF THE INVENTION

The present invention relates to systems and methods for performingquality assurance on patient treatment plans for radiation therapy.

BACKGROUND

Modern radiation therapy is highly customized per patient plan and, withthe advent of intensity modulated radiation therapy and intensitymodulated radiation therapy (IMRT) and volume modulated arc therapy(VMAT), treatment plans can be very complex in nature. This precipitatesthe need for customized and stringent verification to ensure that: 1)the treatment planning system (TPS) calculates the patient doseaccurately; and 2) the delivery system delivers the dose accurately. Theprocess of dose verification of complex plans can be generally calleddose quality assurance (QA), and will be referred to as “Dose QA” fromthis point forward. (Note that a common term in the industry is “IMRTQA”; but this is too limiting in its literal sense as not all modernplans are by definition IMRT.) Modern Dose QA purposes and methods havebeen well described in literature (e.g., B. E. Nelms and J. A. Simon, “Asurvey on planar IMRT QA analysis,” J. Appl. Clin. Med. Phys. 8(3),76-90 (2007); G. A. Ezzell et al., “IMRT commissioning: Multipleinstitution planning and dosimetry comparisons, a report from AAPM TaskGroup 119,” Med. Phys. 36(11), 5359-5373 (2009); V. Feygelman, G. Zhang,C. Stevens, B. E. Nelms, “Evaluation of a new VMAT QA device, or the “X”and “O” array geometries,” J Appl Clin Med Phys. 12(2), 146-168 (2011);B. E. Nelms, H. Zhen, and W. A. Tomé, “Per-beam, planar IMRT QA passingrates do not predict clinically relevant patient dose errors,” Med.Phys. 38(2), 1037-1044 (2011); and H. Zhen, B. E. Nelms, and W. A. Tomé,“Moving from gamma passing rates to patient DVH-based QA metrics inpretreatment dose QA,” Med. Phys. 38(10), 5477-5489 (2011)—the contentsof which references are herein incorporated by reference in theirentirety).

Dose QA performance must be quantified, and quantification requiresmetric(s) of performance. Acceptance of performance level (safety,accuracy, etc.) implies verifying vs. benchmarks and setting clearacceptance criteria. This general strategy, of course, relies on themetric(s) of performance being a good metric, i.e. a goodindicator/predictor of quality. Scientifically and statisticallyspeaking, a good performance metric will be both: a) sensitive and b)specific.

Sensitivity and specificity can be defined using results falling intoone of four main categories, illustrated below. Nelms et al have clearlytranslated these categories in terms of Dose QA (see FIG. 1).

-   -   True Positive: “Sick” person correctly diagnosed as sick (in        Dose QA: unacceptable dose correctly detected as unacceptable).    -   False Positive: “Healthy” person incorrectly diagnosed as sick        (in Dose QA: acceptable dose incorrectly detected as        unacceptable).    -   True Negative: “Healthy” person correctly diagnosed as healthy        (in Dose QA: acceptable dose correctly detected as acceptable).    -   False Negative: “Sick” person incorrectly diagnosed as healthy        (in Dose QA: unacceptable dose incorrectly detected as        acceptable).

Sensitivity can be defined broadly as the ability to correctly detect aproblem. In the case of medicine, sensitivity is the ability of a testto correctly diagnose a sick patient as sick. In Dose QA, sensitivity isthe ability to correctly detect an error when there is an error ofclinical relevance. Sensitivity can be quantified by the followingequation:

${Sensitivity} = \frac{{Number}\mspace{14mu} {of}\mspace{14mu} {True}\mspace{14mu} {Positives}}{\left\lbrack {{{Number}\mspace{14mu} {of}\mspace{14mu} {True}\mspace{14mu} {Positives}} + {{Number}\mspace{14mu} {of}\mspace{14mu} {False}\mspace{14mu} {Negatives}}} \right\rbrack}$

Specificity can be defined broadly as the ability to correctly identifya negative result. In the case of medicine, specificity is the abilityof a test to correctly diagnose a healthy patient as healthy. In DoseQA, specificity is the ability to correctly identify that there are noclinically relevant errors due to the calculation or delivery of thedose. Specificity can be quantified by the following equation:

${Specificity} = \frac{{Number}\mspace{14mu} {of}\mspace{14mu} {True}\mspace{14mu} {Negatives}}{\left\lbrack {{{Number}\mspace{14mu} {of}\mspace{14mu} {True}\mspace{14mu} {Negatives}} + {{Number}\mspace{14mu} {of}\mspace{14mu} {False}\mspace{14mu} {Positives}}} \right\rbrack}$

Typically the conventional QA metric is a “passing rate” (%) ofcalculated dose points vs. measured dose points, where the criteria forpassing are a composite of percent difference, distance-to-agreement(DTA) (e.g., J. Van Dyk et al., “Commissioning and quality assurance oftreatment planning computers,” Int. J. Radiat. Oncol., Biol., Phys.26(2), 261-273 (1993)), or a hybrid metric called the Gamma Index (D. A.Low, W. B. Harms, S. Mutic, and J. A. Purdy, “A technique for thequantitative evaluation of dose distributions,” Med. Phys. 25, 656-661(1998)—the contents of which references are herein incorporated byreference in their entirety). Both the DTA and the Gamma analyses serveto dampen the failures in high dose gradient regions.

In terms of conventional passing rate metrics, the regions of falsepositives and false negatives are illustrated in FIG. 1, as is whatwould be expected if these metrics are well correlated to clinicallyrelevant errors, i.e. errors in dose volume histogram (DVH) results forpatient dose distributions.

Conventional passing rate metrics, though used for many years in IMRT,were never proven in terms of either sensitivity or specificity. Recentstudies of both per-beam planar IMRT methods (e.g., J. J. Kruse, “On theinsensitivity of single field planar dosimetry to IMRT inaccuracies,”Med. Phys. 37(6), 2516-2524 (2010); G. Yan, C. Liu, T. A. Simon, L. C.Peng, C. Fox, and J. G. Li, “On the sensitivity of patient-specific IMRTQA to MLC positioning errors,” J. Appl. Clin. Med. Phys. 10(1), 120-128(2009)—the contents of which references are herein incorporated byreference in their entirety) and 3D composite dosimetry methods haveproven the passing rates to be poor metrics in terms of both sensitivityand specificity, for all common methods. In other words, conventionalmethods/metrics cannot reliably detect significant errors (i.e. theylack sensitivity) nor can they reliably prove accuracy (i.e. they lackspecificity). As such, there is a clear need for improved metrics thatare not only reliable and useful, but also clinically possible andpractical.

The potential limitations of conventional metrics (and especially the3%/3 mm criteria for both %/DTA and Gamma passing rates) were postulatedby Nelms and Simon and, in the same publication, the authors suggestthat moving towards prediction of impact of errors on patient dose andDVH would be more useful and relevant. The authors summarize their pointwell: “The underlying limitation of today's planar IMRT QA approach isthat it does not make the connection between the individual fieldanalyses and the “big picture” of how the patient dose distributionmight be affected—that is, how the plan DVHs might be degraded as aresult of the combined planning and delivery imperfections. Today, theDVH is the critical tool for IMRT dose prescription and plan analysis.An estimated DVH (based on measurements) should perhaps be the new goalof IMRT QA. Although careful field-by-field analyses are now efficientand very effective at detecting differences between the measured fieldsand the planned fields, they do not predict the overall perturbations ofthe volumetric patient dose and DVH statistics. If meaningful standardsfor IMRT QA acceptance testing are to be derived and adopted, thatconnection needs to be made. Estimating DVH perturbations attributableto IMRT QA measurements would be a wise first step in trying tointroduce meaningful standards to IMRT QA, because the benchmarks couldbe set based on more clinically relevant and intuitive endpoints.”

A software product called “3DVH” (Sun Nuclear Corporation, Melbourne,Fla.) is one answer to solving the problems of Dose QA metrics. 3DVHuses the strategy and algorithm called “Planned Dose Perturbation”(PDP), which uses conventional QA data (measured vs. calculated phantomdose) to accurately estimate the impact of any/all observed errors onthe 3D patient dose and DVH. In addition to providing these more usefulmetrics, the aim of 3DVH is to be clinically practical andcost-effective, specifically by allowing existing and ubiquitous QAdevices to gather the required PDP measurement inputs. PDP is furtherdescribed in U.S. Pat. No. 7,945,022, the contents of which are hereinincorporated by reference in their entirety.

One method of dose QA is to deliver all treatment beams at their actualtreatment geometries to a dosimetry phantom that acts as a patientsurrogate. We will call this “true composite” dose QA (as opposed to“single gantry angle composite” where all IMRT beams are delivered atthe same geometry to a flat QA phantom). The dose distribution measuredand calculated in the true composite QA phantom will not be equal to thepatient dose, of course (due to density and size differences), but thereare advantages in delivering a full fraction and verifying the 3D dose,even if it is a phantom dose.

Though IMRT beams are dynamic in nature (moving multi-leaf collimator(MLC) leaves creating intensity modulation) they do not have dynamicbeam geometries; rather, they have static beam angles per beam. However,recently dynamic arc therapy has become more commonplace. In arctherapy, the beam geometry (typically just the gantry angle) changesdynamically during a single treatment beam. Arc therapy with C-armlinear accelerators is often generalized as Volume Modulated Arc Therapy(VMAT), though it is sometimes called by vendor-specific commercialnames such as RapidArc (Varian Medical Systems) or Smart Arc (PhilipsRadiation Oncology Systems). Another common method of dynamic arctherapy that delivers dose through a modulated fan beam that rotates ina helical loop around the patient is called helical tomotherapy, with atrade name Tomotherapy (Accuray).

Because of their dynamic beam geometries, arc therapies lend themselvesto true composite dose QA rather than per-beam planar dose QA.

True composite dose QA dosimetry phantoms have followed, in a sense, thesame evolution as per-beam planar. Namely, the industry has migratedtowards electronic 3D arrays which measure dose without the need fortime-consuming processing. In between the planar film-in-3D phantom eraand the modern 3D electronic array era is wedged a history of using 2Delectronic arrays embedded in 3D phantoms, a limited and semi-inaccuratemethod that remains common due to efficiency and cost-effectiveness. Asummary of methods and devices used in true composite dose QA is givenin Table 1.

TABLE 1 Summary of True Composite Dose QA Methods Method BriefDescription Products Pros Cons Film in 3D Embed planes of Many User cancustomize Inefficient (film Phantom film inside 3D (Options formeasurement processing and phantoms both film and plane(s) analysis)phantoms) User can choose their Dose always in phantom (shape, planes,not material, etc.) volumetric High density measurements 3D Gel 3Dchemical gel BANG Gel High density Inefficient that acts analogousVolumetric (processing requires to a 3D “film” special equipment such asMR or laser scanning) Expensive Limited measurement accuracy 2D Array inPlace a 2D array MapCHECK Inexpensive (use Angular 3D Phantom (ionchamber or MapCHECK2 ubiquitous 2D IMRT dependencies cause diode) insidea 3D PTW 729 QA devices) measurement errors phantom Matrixx EfficientSingle dose plane, not MapPhan* volumetric Octavius* Low detectordensity MultiCube* * Phantoms 3D Array High resolution Delta4 EfficientLimited to fixed (small) detectors ArcCHECK Volumetric detector embeddedin 3D arrangement volumetric Low detector density phantom

The ArcCHECK (AC) device has been well-described in literature (e.g.,Kozelka J, Robinson J, Nelms B, Zhang G, Savitskij D, Feygelman V,“Optimizing the accuracy of a helical diode array dosimeter: acomprehensive calibration methodology coupled with a novel virtualinclinometer,” Med Phys. 38(9), 5021-32 (2011)—the contents of whichreference is hereby incorporated by reference in its entirety). The ACdevice is unique in its detector geometry, which features a cylindricalsurface of diodes with a near-circular cross section of 22 facets and ahelical progression of diodes along the long axis of the cylinder.

SUMMARY OF THE INVENTION

There are advantages and disadvantages of placing detectors in thephantom periphery rather than grouped in the middle of a phantom wherethe critical target dose and organ-at-risk (OAR) dose is typicallylocated in clinical practice. However, the near-circular, peripheral ACdetector surface offers key advantages related to symmetry for treatmentbeams whose geometries differ only in gantry angle. In particular, thedetector geometry is well-suited as data input for Measurement GuidedDose Reconstruction (MGDR), which is an integral part of the AC-PDPalgorithm. MGDR will be discussed in detail below. Accordingly, it is anobject of the present invention to provide systems and methods forcomposite dose QA with 3D arrays.

Schematics diagrams along with important transformations are shown inFIGS. 2 and 3. These figures will help the reader clearly understand theAC detector geometry.

The term “AC-PDP” (short for ArcCHECK-based planned dose perturbation)is a general term that describes the use of data from the AC device asinput into the algorithm that will predict the impact on patient dose.The output of AC-PDP is an estimated 3D patient dose, which when used inconjunction with 3D anatomy structures and images, can be directlycompared to the planned (TPS calculated) patient dose using the analysistools of 3DVH.

AC-PDP is a complex algorithm which is made up of several criticalcomponents. All components are the result of novel technologicalsolutions that are unique to 3DVH and Sun Nuclear Corporation. Thesecomponents will be discussed in detail, one-by-one, in the followingsections of this document. First, however, the high-level roles andrelationships of those components must be made very clear; understandingof the architecture of AC-PDP is essential to not only productdevelopment and regulatory governance, but also to marketing, sales,customer training, and customer support.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates false negatives and false positives in terms ofconventional passing rate metrics. C represents a passing raterequirement that would ensure a DVH error of E or less;

FIG. 2 is a detailed cross-section of the AC phantom and detectors alongwith mathematics for deriving absolute 3D (x, y, z) position from theinteger diode index;

FIG. 3 is a detailed cross-section of the AC phantom and detectors alongwith mathematics for determining which AC diodes surround a beam raygeometry defined by an angle and a longitudinal position;

FIG. 4 is a detailed blueprint of AC-PDP components;

FIG. 5 is a schematic diagram of the critical components of the AC-PDPworkflow (“User”) and algorithm (“Engine”);

FIG. 6 is a schematic diagram showing inputs and outputs of the SYNCfunction;

FIG. 7 is a schematic diagram of the SYNC process;

FIG. 8 is a 2-Beam VMAT plan's Gantry vs. Time illustrated using 3DVH's4D Workspace;

FIG. 9 is a 9-Beam IMRT plan synchronized to ACML;

FIG. 10 is illustrates a discretization of RT Plan dynamic beams intodiscrete beams with static beam geometries;

FIG. 11 illustrates details of building the per sub-beam 3D TERMA+ grid;

FIG. 12 illustrates details of the 3D convolution of TERMA+ 3D dosedeposition kernels to give high-density, relative dose per sub-beam;

FIG. 13 is a schematic of MORPH-NORM, which uses a 3D morphing persub-beam to normalize a relative dose grid into an absolute dose grid;

FIG. 14 illustrates a sub-beam from gantry angle ˜215 degrees (IEC) andits entry and exit absolute dose projections;

FIG. 15 illustrates the sub-beam from FIG. 14 after processing withMORPH-NORM;

FIG. 16 illustrates sub-beam(T) dose grids being summed and the sumbeing scaled by the GCF to give the resulting full-volume virtualmeasurement;

FIG. 17 illustrates the transition from 4D ArcCHECK measurements to afully volume, high-density measurement;

FIG. 18 is an illustration of DIFF-MORPH;

FIG. 19 is a simple schematic giving an intuitive feel for DIFF-MORPH;

FIGS. 20-30 illustrate major workflow steps of AC-PDP;

FIGS. 31-36 illustrate PDP model parameters;

FIGS. 37-40 illustrate advanced/diagnostic tools available in servicemode;

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

FIG. 4 is a detailed blueprint of AC-PDP components, while FIG. 5 is acustom workflow figure that shows both the “user” and “engine” steps ofAC-PDP. These figures will serve as guides to the component-by-componentdescriptions that follow.

Data inputs that are necessary to drive the AC-PDP algorithm aresummarized below in Table 2.

TABLE 2 AC-PDP Data Inputs Data Data Form Data Source Importance PatientPlan DICOM RT Plan TPS Required Patient Structures DICOM RT StructureTPS Required Set Patient Dose DICOM RT Dose TPS Required Patient AxialDICOM3 TPS or Optional Images Scanner ArcCHECK Plan DICOM RT Plan TPSRequired ArcCHECK Dose DICOM RT Dose TPS Required ArcCHECK 4D SNC File(*.acml) SNC Device Required Measurement AC-PDP Model 3DVH Internal 3DVHInternal Required

AC-PDP is explicitly possible because of the “4D” nature of the AC dataupdates. The composite AC dose (housed in the resulting composite *.txtfiles, and used for conventional passing rate analysis for dose at thedetector positions) is, by itself, of no direct use to AC-PDP. As willbecome clear in the following dissection of the algorithm, the keymeasurement inputs required by AC-PDP are: 1) 4D measured absolute dose,i.e. absolute dose per-detector, per high-resolution unit of time, and2) an accurate estimation of the linac gantry vs. time, often called the“Virtual Inclinometer” (VI).

The methods of acquiring the dose vs. time and VI can be found in 3DVHRelease 2.0 Design History File, Algorithm Test Plan and Reports (MGDR,DIFF-MORPH, etc.), the contents of which are herein incorporated byreference in their entirety (see also, U.S. Pat. No. 8,044,359, thecontents of which are also herein incorporated by reference in theirentirety). The method of recording and transferring these data is viathe “ArcCHECK Movie ‘Lite” file (*.acml, or ACML), a file which isrendered after the 4D raw data have been collected. The ACML houses thedose-per-diode vs. time along with the estimated gantry angle vs. time(VI). Both are updated at a default time resolution of 50 msec (thoughother time resolutions can be set). The low density, phantom Dose(T) andthe Gantry(T) data are processed first by the “SYNC” function which isimplemented generally in a class library (called a dynamically linkedlibrary, or DLL) so that it can be used by other SNC softwareapplications.

SYNC will use AC movie data (ACML) in conjunction with the correspondingDICOM RT Plan to “synchronize” the DICOM RT Plan's Beams’ Control Pointsto absolute, corresponding delivery times. The primary outputs of theSync function are shown in FIG. 6 and are summarized below:

-   -   A revised RT Plan object where all the plan's beams' control        points have both “Time” (seconds) and “TimeValidated” (True or        False) values assigned.    -   A “Machine Movie” object with: 1) gantry angle vs. time, and 2)        3D dose cloud (cumulative and differential dose) vs. time for        discrete XYZ points, specifically the positions of the AC        diodes.

SYNC output is important to AC-PDP as it enables (i.e. is an inputto): 1) Discretization of the entire RT Plan's beams into a larger setof many sub-beams, BEAM(T), that are required input for AC-CONV; and 2)MORPH-NORM—the synchronization of 3D high-density dose-to-AC relativedose calculations to the AC measured dose movie, from which selectdetector doses will be used to calibrate each sub-beam's high-density,relative dose grid to be absolute dose.

A mid-level architectural chart of how SYNC works is shown in FIG. 7.The concept of a “beam gene” is used, where a beam gene is a uniquesection of a delivery defined by its geometry and dynamics. The ACML isprocessed into beam genes, as is the RT Plan. Beam genes are matched bytheir genotypes, and if each delivered beam has a unique match in the RTPlan, then the RT Plan's beams' control points are assigned absolutetime values based on a gantry angle-based lookup/interpolation using theVI data, i.e. gantry vs. time, and the gantry angles of the controlpoints. It there is not a unique match between delivered and plannedbeam genes, then attempts are made at finding permutations ofre-ordering (of plan beam genes) and fusing (of delivered beam genes) tocreate a match. (NOTE: Fusing of delivered beam genes is required forbeams that had delivery interruptions making a single beam appear atfirst to be two or more beams.) Also vital to SYNC is thepost-processing of the raw VI data; smoothing and interpolationalgorithms are built into the SYNC function to improve the accuracy ofthe gantry vs. time data and to fill in missing gantry angles.

It is important to note that for static gantry beams (such as IMRT),there is no intra-beam gantry change vs. time and thus only the firstand last control points can be accurately time-stamped. This isimportant in the discussion of subsequent steps of AC-PDP, as it placeslimitations on AC-PDP with static gantry modulated beams. This will bediscussed more in the section “AC-CONV”.

Examples of the SYNC results as seen in windows of 3DVH's “4D Workspace”are shown in FIGS. 8 and 9. In FIG. 8, graph A) is RT Plan controlpoints pre-SYNC; graph B) is Unsynchronized RT Plan loaded alongside thebeam movie object derived from the ACML; and graph C) is RT Plansynchronized to the ACML. Each vertical red hash mark represents an RTPlan control point, while the blue line is plotted from thepost-processed (smoothed and interpolated) gantry vs. time data from theVI.

In FIG. 9, results from a step-and-shoot IMRT plan are shown, so thenumber of control points (seen as vertical red hash marks) is not verydense. Note that the blue curve (ACML gantry vs. time) has manyinterpolated points (thin instead of thick blue line) due to dose notbeing delivered during the “step” portion of the step-and-shootdelivery, i.e. when there is no dose delivered, there are no data tofuel the VI calculation, but gantry angles are filled in later duringSYNC post-processing.

One of the outputs of SYNC is the synchronized RT Plan, where eachtreatment beam's control point is assigned an absolute time that isdirectly related to the 4D low density measured data. Data from thesynchronized RT Plan are consumed by the ArcCHECK Convolution (AC-CONV)component of AC-PDP.

The process of AC-CONV can be separated into its major steps:

-   -   1) Process the dynamic RT Plan into a series of many sub-beams        that are each represented as static beams (fixed beam        geometries, i.e. static gantry);    -   2) For each sub-beam, interpolate and process the control point        data to create modulated fluence per sub-beam;    -   3) Create a 3D impulse function of total energy released per        unit mass in the AC volume using the “TERMA+” parameters and the        off-axis depth kernels as configured in the PDP model specific        to the linac model and energy; and    -   4) Convolve, via Fast Fourier Transform (FFT), the TERMA+ 3D        grid for each sub-beam with the 3D dose scatter depth dose        kernels (also tailored to a linac model and energy) to generate        a relative 3D dose grid for each of the time-resolved sub-beams.        Step 1 is detailed in FIG. 10, Steps 2-3 in FIG. 11, and Step 4        in FIG. 12.

The TERMA+ and 3D convolution algorithms are customized per linac modeland energy and are designed specifically for the plugged AC phantom,i.e. AC plugged to make a homogeneous volume of the phantom plastic. Theoutput of AC-CONV is a high density, volumetric, relative dose grid foreach time-resolved sub-beam. This output is an input to the next step ofAC-PDP, called MORPH-NORM.

The discretization of the RT Plan dynamic beams into many sub-beams isset to give sub-beams that cover roughly 2 gantry degrees (sometimesslightly higher). This is similar to the discretization used by moderntreatment planning systems. As mentioned earlier, IMRT plans alreadyhave multiple (usually between four and 200) dynamic MLC control pointsbut those dynamics change while the beam geometry is static. Thus,gantry vs. time data intra-beam cannot be used to synchronize all thecontrol points to absolute time, and in fact only the beam start and endcan be time-stamped. Thus, the AC-CONV sub-beams reduce to the RT Planbeams and the time resolved calibration to absolute measurements(MORPH-NORM) is limited to very large time intervals.

The high-density, 3D relative dose grids per sub-beam are transformedinto absolute dose as the next stage of AC-PDP. This is done via thecomponent called “MORPH-NORM” which stands for the morphing dosenormalization that converts relative dose values to absolute dose usingthe 4D diode measurements as real-time calibration (normalization) data.

MORPH-NORM is illustrated in FIGS. 13 through 15. MORPH-NORM applies a3D calibration factor grid to each sub-beam's high-density, relativedose. The 3D calibration grid is built from relevant entry and exitdiodes' absolute doses per sub-beam. The resulting output is ahigh-density, absolute dose grid (lower) for the sub-beam. Here,“relevant” in a mathematic sense is defined by the followingrequirements:

-   -   1) The diode's dose is above a qualifying threshold (there is a        different threshold for entry and exit surfaces). Default AC-PDP        settings use a threshold of 80% of the respective surface's        maximum diode dose.    -   2) The diode is not on a high gradient. The gradient of a diode        is quantified in a beam's-eye-view (BEV) sense from the        sub-beam's relative fluence map. Default settings of AC-PDP will        consider a diode on a high gradient if the relative fluence        changes by 10% over a 4 mm radius for that BEV projected to        100 cm. High gradient diodes, even if the diode dose meets the        threshold criterion, are not used in the MORPH-NORM function,        because these diodes are too sensitive with respect to various        geometric considerations such as VI errors, diode placement vs.        nominal, and accuracy of AC setup.

A best-fit entry calibration factor and exit calibration factor arefound for each surface, and all of the sub-beam's beamlets (rays) areprojected from curved surface to curved surface, with calibrationfactors in between being interpolated based on the entry and exitvalues. The nature of the entry and exit being allowed to have differentcalibration factors is the genesis of the “morph” component ofMORPH-NORM. If a single calibration factor was used, it would be only asimple normalization (SIMPLE-NORM), i.e. a scaling of the dose grid andnot a morphing. (NOTE: SIMPLE-NORM AC-PDP can be done while in servicemode, if useful for diagnostic purposes. Also, the can be a furtheroption called “ÜBER-NORM” which allows morphing BEV ray in addition tomorphing with depth, but this would preferably be applied only to fixedgantry IMRT beams at first due to its impact on the speed ofcalculation.)

If for any sub-beam there are not diodes at the entry or exit surfacequalifying to guide MORPH-NORM, then a nominal calibration factor willbe used. This only occurs for very small volume (small segment)sub-beams and the effect of not having qualifying diodes is minimal dueto a relatively small impact on the overall dose.

AC-CONV and MORPH-NORM are performed for each and every discretesub-beam(T) that are used to model the entire treatment fraction. Thenext stage is to complete the high-density virtual measurement in the ACphantom by summing all the sub-beams dose grids and doing somepost-processing. In FIG. 15, the high-density, relative dose gridcalculated by AC-CONV (upper left) and the low-density absolute dosemeasured by AC (upper right) are inputs into the MORPH-NORM function.

The next step in AC-PDP is to generate a full-volume, high-densityabsolute dose grid in the AC phantom using all the sub-beam dose grids.This result could be thought of as a “virtual measurement” or, becauseit is a full volumetric dose, a “virtual BANG gel”. The dose grids fromall sub-beams are summed, and a final processing step is performed whichscales the dose by a global calibration factor (GCF) that best fits thevirtual measurement to the composite (cumulative) doses per diodeposition. The “best fit” is defined by minimizing the cumulative dosedifferences for all diode doses above 30% of the max diode dose, and theGCF will only be applied if at least 12 diodes meet that threshold(otherwise, the GCF is fixed at 1.00). The GCF should always be, and inmost cases is, very close to 1.00. In some rare cases (usually if thetarget volume is very large or very small) it may range from 0.98 to1.02; any GCF outside this range could indicate either bad input data,improperly assigned PDP model, dose measurement/calibration error, orsome other issue. FIG. 16 illustrates the summing of sub-beam dose gridsand post-processing with GCF to generate the composite virtualmeasurement. It is important to note that AC-PDP only reconstructs doseinside the detector surface, and all dose values outside of thatcylinder are set equal to the TPS dose. In the case of FIG. 16, the GCFwas 0.993 which is close to 1.00, as is expected. Note that in thiscase, the reconstructed dose is less than the TPS dose, as evidenced bythe visible discontinuity at the edges of the “reconstruction volume”;this is because AC-PDP does not estimate dose outside of thereconstruction volume (detector surface). The reconstructed dose wasaccurate however, as the AC measurements were actually 3-5% lower (vs.TPS) for this case.

At this point, the entirety of the AC-PDP algorithm has served toproduce a high-density, full-volume, 3D absolute dose in the AC phantom.The required steps are inherently 4D, requiring time-resolved VI andabsolute dose data, along with the time course of high-density relativedose calculations. The generation of a full-volume, high-densityabsolute dose phantom measurement can be generally described as“Measurement-Guided Dose Reconstruction” (MGDR)—see FIG. 17. MGDR is theprimary engine behind AC-PDP algorithm. MGDR is proven to be extremelyaccurate.

Once AC-PDP has generated an accurate MGDR estimate, the hard work ofAC-PDP has been done. However, the depth of knowledge and intuitiveanalysis that can be gained from the full-volume, high-density phantomvirtual measurement can be truly realized if the effects (differencesbetween MGDR and TPS dose-to-phantom) can be used to estimate the impacton patient dose.

In MC-PDP, the perturbation (correction) of TPS calculated dose wasvoxel-by-voxel and beam-by-beam, using correction factors per beamletgarnered from dose planes normal to the CAX. With dynamic beamgeometries and 3D dosimetry, we do not have implicit “pairs” ofmeasurements and calculations per beam geometry, so we cannot use theMC-PDP strategy to estimate the impact on patient dose. However, as itturns out, if an absolute dose difference is known accurately in a 3Dphantom, the errors translate very closely to those in a 3D patient,despite the patient size, shape, and density being different than thephantom. Thus, a 3D dose correction grid based on the phantom can bedirectly applied to the TPS patient dose, using a process calledDIFF-MORPH, meaning to morph the patient dose based on the phantom dosedifferences. DIFF-MORPH in AC-PDP only perturbs (corrects) dose insidethe reconstruction cylinder defined by the AC detector surface, but thisvolume most often contains the entirety of the targets and OARs. Fortarget and/or OAR volumes falling outside the size of the AC detectorcylinder, AC-PDP will not change the dose from the TPS dose. DIFF-MORPHis described in FIGS. 18 and 19.

The simplicity of DIFF-MORPH is striking and can leave a physicistskeptical. However, the DIFF-MORPH strategy (for predicting impact onpatient dose and DVH) is proven accurate over various patient sizes,shapes, and densities.

A failure mode analysis of AC-PDP reveals that the AC-PDP strategy willfail if MGDR results (phantom dose) exhibit differences that are eitherinaccurate or are not manifest in the patient dose. This could be due tothe following conditions, which should therefore be avoided:

-   -   User sets the AC phantom without the isocenter located at the        phantom center;    -   User acquires dose with the AC phantom hollow (i.e. unplugged)        or uses the wrong plug;    -   ACML virtual inclinometer has errors, causing inaccurate        discretization of sub-beams from RT Plan control points, which        would impact the AC-CONV relationship to MORPH-NORM diode doses;    -   AC-CONV model gives inaccurate 3D relative AC dose for sub-beams        due to incorrectly assigned PDP Model for the treatment        machine/energy;    -   ArcCHECK is mis-calibrated, resulting in erroneous absolute        doses that are used in MORPH-NORM;    -   PDP model parameters are not optimal;    -   User's virtual phantom model is incorrectly defined in the TPS,        resulting in errors in the TPS AC dose that will impact        DIFF-MORPH;    -   TPS AC dose is not properly aligned in 3DVH, resulting in data        shifts that will impact DIFF-MORPH;    -   TPS has dose errors that manifest in PMMA/acrylic phantom        geometry but not in a patient.

Now that the technical components of the AC-PDP engine are understood,it is useful to highlight some of the major user workflow steps torecognize at which point the AC-PDP components are taking, or havetaken, place. These are summarized in FIGS. 20 through 30. In FIG. 20,the DICOM RT plan, structure set, and dose are loaded as the referencedose (shown in the 3DVH interface). FIG. 21 illustrates a window showingthe unsynchronized RT Plan, before loading of the ACML file andsubsequent SYNC of the plan's control points.

Referring to FIG. 22, after the ACML is loaded and the plan is SYNC'ed,3DVH prompts the user for the TPS dose-to-AC to be loaded. Here, the TPSdose-to-AC is loaded, but not yet properly aligned. To align TPS thedose-to-AC, the TPS plan-on-AC is loaded in order to extract theisocenter position in the DICOM3 coordinate system, as in FIG. 23. InFIG. 24, the TPS dose-to-AC is shown now properly aligned and ready tobe loaded, allowing the AC-PDP calculation to begin.

While the AC-PDP calculation takes place, the custom progress monitorwill update the sub-beam(T) low density dose as the sub-beams arecalculated (each sub-beam calculation consists of both AC-CONV andMORPH-NORM for that sub-beam) (see FIG. 25). After the AC-PDPcalculation is complete, the GCF and the phantom isocenter dose (bothTPS and MGDR) is displayed. FIG. 26 illustrates an example where the GCFis on the lower range of what is common is shown. This is very rare. TheGCF will typically be 0.99-1.01.

With reference to FIG. 27, after the AC-PDP calculation is complete andloaded, the patient dose can be analyzed in interactive 2D anatomicalplanes and 1D dose profiles. In FIG. 28, analysis of TPS vs. AC-PDPpatient dose using interactive DVH and user-customized “Quick Stats” isseen. The BEV tab for VMAT beams shows integral beam fluence normal tothe CAX over all gantry angles of a beam (as in FIG. 29). For AC-PDPanalysis, the 4D workspace will show the synchronized RT Plan andinteractive 4D tools for analyzing gantry angle, MLC segments,cumulative AC dose, or differential AC dose (as in FIG. 30).

Referring to FIG. 31-36, PDP Model parameters will be described. Theparameters are viewed and/or edited by a user via the following tabs:

-   -   a. FIG. 31—General tab showing the associated machine names and        energies;    -   b. FIG. 32—TERMA+ Parameters tab;    -   c. FIG. 33—Off-Axis Depth Kernels tab, showing the depths used        to define the off-axis kernels (FIG. 34 is an example of an        off-axis depth kernel at a distance depth—off axis profiles at        shallower depths will increase the farther off-axis the ray);    -   d. FIG. 35—Convolution Depth Kernels tab, showing the depths        used to define the dose deposition kernels (FIG. 36 is an        example of convolution radial depth kernel definition);

3DVH has a service mode that allows visibility and access to advancedfeatures that are preferably not provided commercially to the user. Someof the tools are diagnostic in nature, and others designed for researchand testing. Examples of these advanced tools are shown in FIGS. 37through 40. For instance, in FIG. 37 advanced AC-PDP calculationparameter editing can be allowed in service/diagnostic mode, and willappear if activated via the file menu.

Referring to FIG. 38, AC-PDP 4D diagnostics will appear if in servicemode and the option activated via the file menu. These can be used todiagnose problems in the input data or AC-PDP calculation components.The Top Row in FIG. 38 shows MORPH-NORM calibration factors at entry(red) and exit (blue) surfaces. The red and blue curves should be veryclose to one another, and if not it could signify VI or SYNC errors. TheMiddle Row shows Max diode doses at entry and exit surfaces, giving someidea of the peak dose per sub-beam. The Bottom Row shows the number ofqualifying calibration (MORPH-NORM) diodes at entry and exit surfaces;usually the number of calibration diodes at the exit surface should belarger than the number at the entry surface due to geometric projectionof the sub-beam dose pattern.

When optimizing AC-PDP parameters, PDDs for user-input field sizes canbe quickly calculated and displayed (see FIG. 39). These are driven byall parameters (TERMA+, Off-Axis, and Convolution) but fitting thesecurves to measurements is most useful when optimizing the convolutiondepth kernels. When optimizing AC-PDP parameters, dose profiles atentry, isocenter, and exit distances for user-input field sizes can bequickly calculated and displayed (as in FIG. 40). Using isocenter/entryand exit/entry ratios is an important tool when modeling AC-PDPparameters.

In general, the foregoing description is provided for exemplary andillustrative purposes; the present invention is not necessarily limitedthereto. Rather, those skilled in the art will appreciate thatadditional modifications, as well as adaptations for particularcircumstances, will fall within the scope of the invention as hereinshown and described.

What is claimed is:
 1. A method for performing composite dose qualityassurance with a three-dimensional (3D) radiation detector array, themethod comprising: delivering, from a moving radiation source, aradiation fraction to the 3D array according to a radiation treatment(RT) plan; measuring absolute dose per detector of the 3D array, perunit of time; determining a radiation source emission angle per unit oftime; synchronizing the RT plan with the measured absolute doses anddetermined radiation source emission angles to determine an absolutetime for a control point of each beam of the synchronized RT plan;converting the beams of the synchronized RT plan into a series ofsub-beams; generating a 3D relative dose grid for each of the sub-beams;applying a calibration factor grid to each of the 3D relative dose gridsto determine a 3D absolute dose grid for each of the sub-beams; summingthe 3D absolute dose grids to generate a 3D absolute dose deposited inthe 3D array; and determining a 3D dose correction grid for applicationto the RT plan based on the 3D absolute dose.